A policy gradient method is a type of reinforcement learning algorithm that focuses on optimizing the policy directly rather than the value function. In reinforcement learning, a policy defines how an agent decides which action to take in a given environment state. Instead of deriving a policy from a value function, like in Q-learning, policy gradient methods adjust the policy parameters through gradient ascent, aiming to maximize the expected rewards from the actions taken. Essentially, these methods provide a systematic approach to find the best policy by updating it based on the feedback from the environment.
The fundamental idea behind policy gradient methods is to compute the gradient of the expected return (or reward) with respect to the policy parameters. This involves defining a performance measure, usually the cumulative reward that the agent receives over time. For example, if the agent follows a certain policy and then accumulates a series of rewards, each action taken has a specific contribution to the overall returns. By using a technique such as the REINFORCE algorithm, the policy is updated in the direction that maximizes these returns, thus leading to improved action choices in the long run.
There are various forms of policy gradient methods, including the vanilla policy gradient and more advanced variations like Actor-Critic methods. In Actor-Critic, for example, there are two models: the "Actor," which is responsible for updating the policy, and the "Critic," which evaluates the actions taken by the Actor and helps reduce the variance in the updates. Another popular approach is Proximal Policy Optimization (PPO), which introduces constraints to the updates, improving stability and performance. Overall, policy gradient methods are powerful tools in reinforcement learning, particularly in environments where the action space is large or continuous.